def __init__(self, path='ConformerS.h5'):
# fetch and load the config of the model
config = Config('tamil_tech/configs/conformer_new_config.yml', learning=True)
# load speech and text featurizers
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
text_featurizer = CharFeaturizer(config.decoder_config)
# check if model already exists in given path, else download the model in the given path
if os.path.exists(path):
pass
else:
print("Downloading Model...")
file_id = config.file_id
download_file_from_google_drive(file_id, path)
print("Downloaded Model Successfully...")
# load model using config
self.model = Conformer(**config.model_config, vocabulary_size=text_featurizer.num_classes)
# set shape of the featurizer and build the model
self.model._build(speech_featurizer.shape)
# load weights of the model
self.model.load_weights(path, by_name=True)
# display model summary
self.model.summary(line_length=120)
# set featurizers for the model
self.model.add_featurizers(speech_featurizer, text_featurizer)
print("Loaded Model...!")
def test_conformer():
config = Config(DEFAULT_YAML, learning=False)
text_featurizer = CharFeaturizer(config.decoder_config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
model = Conformer(vocabulary_size=text_featurizer.num_classes, **config.model_config)
model._build(speech_featurizer.shape)
model.summary(line_length=150)
model.add_featurizers(speech_featurizer=speech_featurizer, text_featurizer=text_featurizer)
concrete_func = model.make_tflite_function(timestamp=False).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.convert()
print("Converted successfully with no timestamp")
concrete_func = model.make_tflite_function(timestamp=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.convert()
print("Converted successfully with timestamp")
def build_am(self, config_path, model_path):
config = Config(config_path, learning=False)
conformer = Conformer(**config.model_config, vocabulary_size=1031)
conformer._build(self.speech_featurizer.shape)
print('loading am...')
conformer.load_weights(model_path, by_name=True)
return conformer
assert args.saved
if args.tfrecords:
test_dataset = ASRTFRecordDataset(
data_paths=config.learning_config.dataset_config.test_paths,
tfrecords_dir=config.learning_config.dataset_config.tfrecords_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="test",
shuffle=False)
else:
test_dataset = ASRSliceDataset(
data_paths=config.learning_config.dataset_config.test_paths,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="test",
shuffle=False)
# build model
conformer = Conformer(**config.model_config,
vocabulary_size=text_featurizer.num_classes)
conformer._build(speech_featurizer.shape)
conformer.load_weights(args.saved, by_name=True)
conformer.summary(line_length=120)
conformer.add_featurizers(speech_featurizer, text_featurizer)
conformer_tester = BaseTester(config=config.learning_config.running_config,
output_name=args.output_name)
conformer_tester.compile(conformer)
conformer_tester.run(test_dataset)
def main():
parser = argparse.ArgumentParser(prog="Conformer Training")
parser.add_argument("--config",
type=str,
default=DEFAULT_YAML,
help="The file path of model configuration file")
parser.add_argument("--max_ckpts",
type=int,
default=10,
help="Max number of checkpoints to keep")
parser.add_argument("--tbs",
type=int,
default=None,
help="Train batch size per replica")
parser.add_argument("--ebs",
type=int,
default=None,
help="Evaluation batch size per replica")
parser.add_argument("--acs",
type=int,
default=None,
help="Train accumulation steps")
parser.add_argument("--devices",
type=int,
nargs="*",
default=[0],
help="Devices' ids to apply distributed training")
parser.add_argument("--mxp",
default=False,
action="store_true",
help="Enable mixed precision")
parser.add_argument("--subwords",
type=str,
default=None,
help="Path to file that stores generated subwords")
parser.add_argument("--subwords_corpus",
nargs="*",
type=str,
default=[],
help="Transcript files for generating subwords")
parser.add_argument(
"--train-dir",
'-td',
nargs='*',
default=["en_ng_male_train.tsv", "en_ng_female_train.tsv"])
parser.add_argument("--train-reg-dir",
'-trd',
nargs='*',
default=[
"libritts_train-clean-100.tsv",
"libritts_train-clean-360.tsv",
"libritts_train-other-500.tsv"
])
parser.add_argument(
"--dev-dir",
'-dd',
nargs='*',
default=["en_ng_male_eval.tsv", "en_ng_female_eval.tsv"])
parser.add_argument("--dev-reg-dir",
'-drd',
nargs='*',
default=["libritts_test-other.tsv"])
args = parser.parse_args()
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": args.mxp})
strategy = setup_strategy(args.devices)
config = Config(args.config, learning=True)
config.train_dir = args.train_dir
config.dev_dir = args.dev_dir
config.train_reg_dir = args.train_reg_dir
config.dev_reg_dir = args.dev_reg_dir
with open(config.speech_config) as f:
speech_config = yaml.load(f, Loader=yaml.Loader)
speech_featurizer = TFSpeechFeaturizer(speech_config)
if args.subwords and os.path.exists(args.subwords):
print("Loading subwords ...")
text_featurizer = SubwordFeaturizer.load_from_file(
config.decoder_config, args.subwords)
else:
print("Generating subwords ...")
text_featurizer = SubwordFeaturizer.build_from_corpus(
config.decoder_config, corpus_files=args.subwords_corpus)
text_featurizer.save_to_file(args.subwords)
train_dataset = Dataset(data_paths=config.train_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
augmentations=config.learning_config.augmentations,
stage="train",
cache=False,
shuffle=False)
train_reg_dataset = DatasetInf(
data_paths=config.train_reg_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
augmentations=config.learning_config.augmentations,
stage="train",
cache=False,
shuffle=False)
eval_dataset = Dataset(data_paths=config.dev_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="eval",
cache=False,
shuffle=False)
eval_reg_dataset = DatasetInf(
data_paths=config.dev_reg_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
augmentations=config.learning_config.augmentations,
stage="eval",
cache=False,
shuffle=False)
conformer_trainer = MultiReaderTransducerTrainer(
config=config.learning_config.running_config,
text_featurizer=text_featurizer,
strategy=strategy)
with conformer_trainer.strategy.scope():
# build model
conformer = Conformer(**config.model_config,
vocabulary_size=text_featurizer.num_classes)
conformer._build(speech_featurizer.shape)
conformer.summary(line_length=120)
optimizer = tf.keras.optimizers.Adam(
TransformerSchedule(d_model=conformer.dmodel,
warmup_steps=config.learning_config.
optimizer_config["warmup_steps"],
max_lr=(0.05 / math.sqrt(conformer.dmodel))),
beta_1=config.learning_config.optimizer_config["beta1"],
beta_2=config.learning_config.optimizer_config["beta2"],
epsilon=config.learning_config.optimizer_config["epsilon"])
conformer_trainer.compile(model=conformer,
optimizer=optimizer,
max_to_keep=args.max_ckpts)
conformer_trainer.fit(
train_dataset,
train_reg_dataset,
# alpha for regularising dataset; alpha = 1 for training dataset
1.,
eval_dataset,
eval_reg_dataset,
train_bs=args.tbs,
eval_bs=args.ebs,
train_acs=args.acs)
"feature_type": "log_mel_spectrogram",
"preemphasis": 0.97,
"normalize_signal": True,
"normalize_feature": True,
"normalize_per_feature": False
})
# i = tf.keras.Input(shape=[None, 80, 1])
# o = Conv2dSubsampling(144)(i)
# encoder = tf.keras.Model(inputs=i, outputs=o)
# model = Transducer(encoder=encoder, vocabulary_size=text_featurizer.num_classes)
model = Conformer(
subsampling={"type": "conv2d", "filters": 144, "kernel_size": 3,
"strides": 2},
num_blocks=1,
vocabulary_size=text_featurizer.num_classes)
model._build(speech_featurizer.shape)
model.summary(line_length=150)
model.save_weights("/tmp/transducer.h5")
model.add_featurizers(
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer
)
# features = tf.zeros(shape=[5, 50, 80, 1], dtype=tf.float32)
# pred = model.recognize(features)
args = parser.parse_args()
from tensorflow_asr.configs.config import Config
from tensorflow_asr.featurizers.speech_featurizers import read_raw_audio
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer
from tensorflow_asr.featurizers.text_featurizers import CharFeaturizer, SubwordFeaturizer
from tensorflow_asr.models.conformer import Conformer
config = Config(args.config, learning=False)
with open(config.speech_config) as f:
speech_config = yaml.load(f, Loader=yaml.Loader)
with tf.device('/cpu:0'):
speech_featurizer = TFSpeechFeaturizer(speech_config)
# build model
conformer = Conformer(**config.model_config, vocabulary_size=1031)
conformer._build(speech_featurizer.shape)
conformer.load_weights(args.saved, by_name=True)
encoder = conformer.encoder
# encoder.summary(line_length=120)
@tf.function(input_signature=[
tf.TensorSpec(shape=[None], dtype=tf.float32, name="signal")
])
def extract_from_audio(signal):
with tf.device('/cpu:0'):
features = speech_featurizer.tf_extract(signal)
return extract_from_mel(features)
help="Output to save whole model")
args = parser.parse_args()
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": args.mxp})
setup_devices([args.device], cpu=args.cpu)
from tensorflow_asr.configs.user_config import UserConfig
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer
from tensorflow_asr.featurizers.text_featurizers import CharFeaturizer
from tensorflow_asr.models.conformer import Conformer
config = UserConfig(DEFAULT_YAML, args.config, learning=True)
speech_featurizer = TFSpeechFeaturizer(config["speech_config"])
text_featurizer = CharFeaturizer(config["decoder_config"])
tf.random.set_seed(0)
assert args.saved
# build model
conformer = Conformer(vocabulary_size=text_featurizer.num_classes,
**config["model_config"])
conformer._build(speech_featurizer.shape)
conformer.load_weights(args.saved, by_name=True)
conformer.summary(line_length=150)
conformer.save(args.output)
print(f"Saved whole model to {args.output}")
def test_conformer():
config = Config(DEFAULT_YAML)
text_featurizer = CharFeaturizer(config.decoder_config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
model = Conformer(vocabulary_size=text_featurizer.num_classes,
**config.model_config)
model._build(speech_featurizer.shape)
model.summary(line_length=150)
model.add_featurizers(speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer)
concrete_func = model.make_tflite_function(
timestamp=False).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
tflite = converter.convert()
print("Converted successfully with no timestamp")
concrete_func = model.make_tflite_function(
timestamp=True).get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
converter.convert()
print("Converted successfully with timestamp")
tflitemodel = tf.lite.Interpreter(model_content=tflite)
signal = tf.random.normal([4000])
input_details = tflitemodel.get_input_details()
output_details = tflitemodel.get_output_details()
tflitemodel.resize_tensor_input(input_details[0]["index"], [4000])
tflitemodel.allocate_tensors()
tflitemodel.set_tensor(input_details[0]["index"], signal)
tflitemodel.set_tensor(input_details[1]["index"],
tf.constant(text_featurizer.blank, dtype=tf.int32))
tflitemodel.set_tensor(
input_details[2]["index"],
tf.zeros([
config.model_config["prediction_num_rnns"], 2, 1,
config.model_config["prediction_rnn_units"]
],
dtype=tf.float32))
tflitemodel.invoke()
hyp = tflitemodel.get_tensor(output_details[0]["index"])
print(hyp)